East China Jiaotong University is a university in Nanchang, Jiangxi province, China. It was formerly Shanghai Railway Institute, which was moved from Shanghai to Nanchang in 1971 and adopted the current name. Wikipedia.

This article analyzes the characteristics of modern production system. It points out that structure of modern production system is composed of heterogeneous groups and production information that is dynamically changing. As a result, there is a bottleneck in most existing monitoring systems that it is hardly easy to acquire and exchange terminal data. In order to solve this problem, this article has proposed a Multi-Agent based system that is designed for monitoring production management. This article shows that, with the Radio Frequency Identification (RFID) technology whose function is to track and identify the production process and to get real-time data, the Multi-Agent based monitoring system can help the enterprises to make decision and to obtain real-time information of the production.

In this study, the Fe/Cu/C and Fe/Al/C inner micro-electrolysis systems were used to treat actual oilfield produced water to evaluate the feasibility of the technology. Effects of reaction time, pH value, the dosage of metals and activated carbon, and Fe:C mass ratio on the treatment efficiency of wastewater were studied. The results showed that the optimum conditions were reaction time 120 min, initial solution pH 4.0, Fe dosage 13.3 g/L, activated carbon dosage 6.7 g/L, Cu dosage 2.0 g/L or Al dosage 1.0 g/L. Under the optimum conditions, the removal efficiencies of chemical oxygen demand (COD) were 39.3%, 49.7% and 52.6% in the Fe/C, Fe/Cu/C and Fe/Al/C processes, respectively. Meanwhile, the ratio of five-day biochemical oxygen demand to COD was raised from 0.18 to above 0.35, which created favourable conditions for the subsequent biological treatment. All these led to an easy maintenance and low operational cost.

Wireless sensor network (WSN) is a key enabling technology for ambient intelligence, where localization of sensor nodes is a fundamental and essential issue. Aiming at localization deficiency of DV-Based algorithm which are caused by the shortest path distance substituting for Euclidean distance, combing the advantages of error inhibition of MSO (mass-spring model), in this paper we propo se a DV-MSO localization algorithm, which not restrains the localization error of DV-Hop algorithm, but also overcomes the defect of MSO algorithm that it is easy to fall into local optimum. Simulation results demonstrate that the localization error of unknown nodes is reduced greatly by the proposed method.